{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:43:58Z","timestamp":1742913838406,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819610679"},{"type":"electronic","value":"9789819610686"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-1068-6_26","type":"book-chapter","created":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T04:04:00Z","timestamp":1738901040000},"page":"275-285","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unknown-Aware Diverse Prompt Learning for\u00a0Open-Set Single Domain Generalization-Based Face Anti-spoofing"],"prefix":"10.1007","author":[{"given":"Fangling","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weining","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,8]]},"reference":[{"issue":"5","key":"26_CR1","first-page":"5609","volume":"45","author":"Z Yu","year":"2023","unstructured":"Yu, Z., Qin, Y., Li, X., Zhao, C., Lei, Z., Zhao, G.: Deep learning for face anti-spoofing: a survey. TPAMI 45(5), 5609\u20135631 (2023)","journal-title":"TPAMI"},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"Jia, Y.,\u00a0Zhang, J.,\u00a0Shan, S.,\u00a0Chen, X.: Unified unsupervised and semi-supervised domain adaptation network for cross-scenario face anti-spoofing. PR 115, 107888 (2021)","DOI":"10.1016\/j.patcog.2021.107888"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Liu, Y.,\u00a0Stehouwer, J.,\u00a0Jourabloo, A.,\u00a0Liu, X.: Deep tree learning for zero-shot face anti-spoofing. In: CVPR, pp. 4680\u20134689 (2019)","DOI":"10.1109\/CVPR.2019.00481"},{"key":"26_CR4","unstructured":"Jiang, F., et al.: Open-set single-domain generalization for robust face anti-spoofing. IJCV, 1\u201322 (2024)"},{"key":"26_CR5","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: ICML, pp. 8748\u20138763 (2021)"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Srivatsan, K., Naseer, M., Nandakumar, K.: Flip: cross-domain face anti-spoofing with language guidance. In: ICCV, pp. 19\u00a0685\u201319\u00a0696 (2023)","DOI":"10.1109\/ICCV51070.2023.01803"},{"key":"26_CR7","doi-asserted-by":"crossref","unstructured":"Liu, A., et al.: CFPL-FAS: class free prompt learning for generalizable face anti-spoofing. In: CVPR, pp. 222\u2013232 (2024)","DOI":"10.1109\/CVPR52733.2024.00029"},{"issue":"1","key":"26_CR8","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1007\/s44267-023-00025-8","volume":"1","author":"P Yan","year":"2023","unstructured":"Yan, P., Liu, X., Zhang, P., Lu, H.: Learning convolutional multi-level transformers for image-based person re-identification. Vis. Intell. 1(1), 24 (2023)","journal-title":"Vis. Intell."},{"key":"26_CR9","doi-asserted-by":"crossref","unstructured":"Liu, Y., Jourabloo, A., Liu, X.: Learning deep models for face anti-spoofing: binary or auxiliary supervision. In: CVPR, pp. 389\u2013398 (2018)","DOI":"10.1109\/CVPR.2018.00048"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Yu, Z., Cai, R., Cui, Y., Liu, A., Chen, C.: Visual prompt flexible-modal face anti-spoofing. arXiv preprint arXiv:2307.13958 (2023)","DOI":"10.1109\/TDSC.2024.3520534"},{"issue":"7","key":"26_CR11","first-page":"1794","volume":"13","author":"H Li","year":"2018","unstructured":"Li, H., Li, W., Cao, H., Wang, S., Huang, F., Kot, A.C.: Unsupervised domain adaptation for face anti-spoofing. TIFS 13(7), 1794\u20131809 (2018)","journal-title":"TIFS"},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Li, Q., Wang, W., Xu, C., Sun, Z., Yang, M.-H.: Learning disentangled representation for one-shot progressive face swapping. In: TPAMI (2024)","DOI":"10.1109\/TPAMI.2024.3404334"},{"key":"26_CR13","unstructured":"Li, Q., Sun, Z., He, R., Tan, T.: Deep supervised discrete hashing. In: NeurIPS, pp. 2479\u20132488 (2017)"},{"key":"26_CR14","unstructured":"Zhang, J., Huang, J., Jin, S., Lu, S.: Vision-language models for vision tasks: a survey. arXiv preprint arXiv:2304.00685 (2023)"},{"issue":"1","key":"26_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s44267-024-00058-7","volume":"2","author":"Z Li","year":"2024","unstructured":"Li, Z., Lv, X., Yu, W., Liu, Q., Lin, J., Zhang, S.: Face shape transfer via semantic warping. Vis. Intell. 2(1), 1\u201311 (2024)","journal-title":"Vis. Intell."},{"issue":"1","key":"26_CR16","first-page":"1","volume":"21","author":"S Peng","year":"2024","unstructured":"Peng, S., Zhu, X., Yi, D., Qian, C., Lei, Z.: Formulating facial mesh tracking as a differentiable optimization problem: a backpropagation-based solution. Vis. Intell. 21(1), 1\u201312 (2024)","journal-title":"Vis. Intell."},{"issue":"9","key":"26_CR17","doi-asserted-by":"publisher","first-page":"2337","DOI":"10.1007\/s11263-022-01653-1","volume":"130","author":"K Zhou","year":"2022","unstructured":"Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Learning to prompt for vision-language models. IJCV 130(9), 2337\u20132348 (2022)","journal-title":"IJCV"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Conditional prompt learning for vision-language models. arXiv preprint arXiv:2203.05557 (2022)","DOI":"10.1109\/CVPR52688.2022.01631"},{"key":"26_CR19","unstructured":"Bahng, H., Jahanian, A., Sankaranarayanan, S., Isola, P.: Exploring visual prompts for adapting large-scale models. arXiv preprint arXiv:2203.17274 (2022)"},{"key":"26_CR20","unstructured":"Zang, Y., Li, W., Zhou, K., Huang, C., Loy, C.C.: Unified vision and language prompt learning. arXiv preprint arXiv:2210.07225 (2022)"},{"key":"26_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., Li, S.Z.: A face antispoofing database with diverse attacks. In: ICB, pp. 26\u201331 (2012)","DOI":"10.1109\/ICB.2012.6199754"},{"key":"26_CR22","unstructured":"Chingovska, I., Anjos, A., Marcel, S.: On the effectiveness of local binary patterns in face anti-spoofing. In: Proceedings of International Conference of the Biometrics Special Interest Group, pp. 1\u20137 (2012)"},{"issue":"4","key":"26_CR23","first-page":"746","volume":"10","author":"D Wen","year":"2015","unstructured":"Wen, D., Han, H., Jain, A.K.: Face spoof detection with image distortion analysis. TIFS 10(4), 746\u2013761 (2015)","journal-title":"TIFS"},{"key":"26_CR24","doi-asserted-by":"crossref","unstructured":"Boulkenafet, Z., Komulainen, J., Li, L., Feng, X., Hadid, A.: OULU-NPU: a mobile face presentation attack database with real-world variations. In: FG, pp. 612\u2013618 (2017)","DOI":"10.1109\/FG.2017.77"},{"issue":"4","key":"26_CR25","first-page":"399","volume":"2","author":"G Heusch","year":"2020","unstructured":"Heusch, G., George, A., Geissb\u00fchler, D., Mostaani, Z., Marcel, S.: Deep models and shortwave infrared information to detect face presentation attacks. TBIOM 2(4), 399\u2013409 (2020)","journal-title":"TBIOM"},{"issue":"8","key":"26_CR26","first-page":"1818","volume":"11","author":"Z Boulkenafet","year":"2016","unstructured":"Boulkenafet, Z., Komulainen, J., Hadid, A.: Face spoofing detection using colour texture analysis. TIFS 11(8), 1818\u20131830 (2016)","journal-title":"TIFS"},{"key":"26_CR27","first-page":"56","volume":"16","author":"G Wang","year":"2020","unstructured":"Wang, G., Han, H., Shan, S., Chen, X.: Unsupervised adversarial domain adaptation for cross-domain face presentation attack detection. TIFS 16, 56\u201369 (2020)","journal-title":"TIFS"},{"key":"26_CR28","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Lu, Y.-D., Yang, S.-T., Lai, S.-H.: PatchNet: a simple face anti-spoofing framework via fine-grained patch recognition. In: CVPR, pp. 20\u00a0281\u201320\u00a0290 (2022)","DOI":"10.1109\/CVPR52688.2022.01964"},{"key":"26_CR29","doi-asserted-by":"crossref","unstructured":"Huang, H.-P., et al.: Adaptive transformers for robust few-shot cross-domain face anti-spoofing. In: ECCV, pp. 37\u201354 (2022)","DOI":"10.1007\/978-3-031-19778-9_3"}],"container-title":["Lecture Notes in Computer Science","Biometric Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-1068-6_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T04:04:17Z","timestamp":1738901057000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-1068-6_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819610679","9789819610686"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-1068-6_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"8 February 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCBR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Biometric Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccbr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ccbr99.cn\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}